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pandas 应用并分配给多个列

[英]pandas apply and assign to multiple columns

I have a dataframe with column like:我有一个 dataframe ,其列如下:

col 
a2_3
f4_4
c4_1

I want to add two columns from this column, like so:我想从该列中添加两列,如下所示:

col   col1   col2   col3
a2_3    a      2     3
f4_4    f      4     4
c4_1    c      4     1

The following does not work:以下不起作用:

df[['col1', 'col2', 'col3']] = df['col'].apply(lambda s: (s[1], *s[1:].split("_")) )

How can I assign series of tuples to new columns?如何将一系列元组分配给新列?

Here apply is not necessary, first indexing with str and then use Series.str.split with expand=True :这里apply不是必需的,首先使用str进行索引,然后使用Series.str.splitexpand=True

df[['col1', 'col2']] = df['col'].str[1:].str.split("_", expand=True)
print (df)
    col col1 col2
0  a2_3    2    3
1  f4_4    4    4
2  c4_1    4    1

Your solution is possible with Series constructor, but it is slow:您的解决方案可以使用Series构造函数,但速度很慢:

df[['col1', 'col2']] = df['col'].apply(lambda s: pd.Series(s[1:].split("_")))

Faster is use DataFrame constructor:更快的是使用 DataFrame 构造函数:

df1 = pd.DataFrame(df['col'].apply(lambda s: s[1:].split("_")).tolist(), index=df.index)
df[['col1', 'col2']] = df1

Or list comprehension:或列表理解:

df[['col1', 'col2']] = pd.DataFrame([s[1:].split("_") for s in df['col']], index=df.index)

EDIT: Solution is similar:编辑:解决方案类似:

L = df['col'].apply(lambda s: (s[0], *s[1:].split("_"))).tolist()
df[['col1', 'col2', 'col3']] = pd.DataFrame(L, index=df.index)

df[['col1', 'col2', 'col3']] = pd.DataFrame([(s[0], *s[1:].split("_")) for s in df['col']], 
                                 index=df.index)
print (df)
    col col1 col2 col3
0  a2_3    a    2    3
1  f4_4    f    4    4
2  c4_1    c    4    1

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